import pandas as pd
import matplotlib.pyplot as plt
 
# Data input
hic_data = {
    "Country": ["Spain", "Norway", "Luxembourg", "Switzerland", "Ireland"],
    "Mean Age at First Birth": [31.5, 30.1, 31.4, 31.3, 31.6]
}
 
lic_mic_data = {
    "Country": ["Rwanda", "Algeria", "Ukraine", "Philippines", "Iran"],
    "Mean Age at First Birth": [22.5, 24.5, 24.9, 22.3, 27.4]
}
 
# Convert to DataFrames
hic_df = pd.DataFrame(hic_data)
lic_mic_df = pd.DataFrame(lic_mic_data)
 
# Calculate overall means
hic_mean = hic_df["Mean Age at First Birth"].mean()
lic_mic_mean = lic_mic_df["Mean Age at First Birth"].mean()
 
# Combine for display
summary_df = pd.DataFrame({
    "Group": ["High-Income Countries (HICs)", "Low- and Middle-Income Countries (LICs/MICs)"],
    "Overall Mean Age at First Birth": [hic_mean, lic_mic_mean]
})
 
# Plot chart
plt.figure(figsize=(10,6))
plt.bar(hic_df["Country"], hic_df["Mean Age at First Birth"], label="HICs", alpha=0.7)
plt.bar(lic_mic_df["Country"], lic_mic_df["Mean Age at First Birth"], label="LICs/MICs", alpha=0.7)
plt.axhline(hic_mean, color='blue', linestyle='--', linewidth=1.2, label=f"HIC Mean: {hic_mean:.1f}")
plt.axhline(lic_mic_mean, color='orange', linestyle='--', linewidth=1.2, label=f"LIC/MIC Mean: {lic_mic_mean:.1f}")
plt.title("Mean Age at First Birth by Country", fontsize=14)
plt.ylabel("Mean Age at First Birth (years)", fontsize=12)
plt.xticks(rotation=45)
plt.legend()
plt.tight_layout()
 
import caas_jupyter_tools
caas_jupyter_tools.display_dataframe_to_user("Summary of Overall Mean Ages", summary_df)
 
plt.show()
 

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